Forecasting Model Selection with Variables Impact to Predict Electricity Demand at Rajshahi City of Bangladesh

Md Rasel Sarkar; Lafifa Margia Orpa; Rifat Afroz Orpe

doi:10.38032/jea.2023.03.002

Authors

Md Rasel Sarkar Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
Lafifa Margia Orpa Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh
Rifat Afroz Orpe Department of Industrial & Production Engineering, Rajshahi University of Engineering & Technology, Rajshahi, Bangladesh

DOI:

https://doi.org/10.38032/jea.2023.03.002

Keywords:

Forecasting, Soft Computing, Fuzzy Linear Regression, Root Mean Square Error, Correlation Coefficient, Forecasting Error

Abstract

The purpose of this study is to forecast electricity demand by using the best-selected method which untangles all the factors that affect electricity demand. Three different methods traditional methods (Multiple Regression Model), modified-traditional methods (ARMA), and soft computing method (Fuzzy Linear Regression Model) are selected for prediction. Environmental parameters like temperature, humidity, and wind speed are included as variables as Rajshahi has very impactful weather. The impact of each variable was calculated from their standardized values to know the effect of environmental parameters. The accuracy of the three forecasting models is compared by different statistical measures of errors. Using Mean Absolute Percentage Error (MAPE), the errors of the Multiple Regression Model, ARMA, and Fuzzy Linear Regression (FLR) Model are 6.85%, 22.24%, and 4.45%. The other three measures of error also give the FLR gives the best results. Finally, the electricity demand of Rajshahi City for the next five years is forecasted using the Fuzzy Linear Regression Model.

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